A function which I cannot change returns Scalaz Reader,
type Action[A] = Reader[Session, A]
def findAccount(s: String): Action[Account] =
Reader((session: Session) => Account(s))
I want create a new function based on findAccount(...) to return ReaderT[Option, Session, A] as in
type ActionT[A] = ReaderT[Option, Session, A]
def findAccountT(s: String): ActionT[Account] = findAccount(s).map(Option(_))
because eventually I want to do this,
def findAccBalT(accountNumber: String) = for {
acc <- findAccountT(accountNumber)
bal <- findBalanceT(acc)
} yield bal
How do I proceed? Does it make sense? Thanks
Full disclosure,
import scalaz._
import Scalaz._
trait Session {
def doSomething(): Unit
}
case class Account(number: String) extends AnyVal
case class Amount(value: Int, currency: String)
case class DBSession() extends Session {
override def doSomething = println("writing to db")
}
type Action[A] = Reader[Session, A]
type ActionT[A] = ReaderT[Option, Session, A]
def findAccount(s: String): Action[Account] =
Reader((session: Session) => Account(s))
def findBalance(account: Account): Action[Amount] =
Reader((session: Session) => Amount(333, "S$"))
// failed
def findAccountT(s: String): ActionT[Account] = findAccount(s).map(Option(_))
// failed
def findBalanceT(account: Account): ActionT[Amount] = findBalance(account).map(Option(_))
// failed
def findAccBalT(accountNumber: String) = for {
acc <- findAccountT(accountNumber)
bal <- findBalanceT(acc)
} yield bal
Short answer: You can use mapK.
Reader[A] is a type alias for ReaderT[Id, A] and ReaderT is an alias for Kleisli. Id[A] is the same as A.
In the Kleisli ScalaDoc we find mapK :
def mapK[N[_], C](f: (M[B]) => N[C]): Kleisli[N, A, C]
Since we know that Reader[Session, A] is the same as Kleisli[Id, Session, A] we can use mapK to go to Kleisli[Option, Session, A] :
import scalaz._, Scalaz._
type Session = String
type Action[A] = Reader[Session, A]
type ActionT[A] = ReaderT[Option, Session, A]
val action: Action[String] = Reader(s => s)
val actionT: ActionT[String] = action mapK Option.apply
Related
I'm reading the scala-with-cats book and follow it's exercise. When I come to the case study: data validation, I encounter some problems.
Here is my entire code (just the same with the book):
package org.scala.ch10.final_recap
import cats.Semigroup
import cats.data.Validated
import cats.data.Validated._
import cats.data.Kleisli
import cats.data.NonEmptyList
import cats.instances.either._
import cats.syntax.apply._
import cats.syntax.semigroup._
import cats.syntax.validated._
sealed trait Predicate[E, A] {
import Predicate._
def and(that: Predicate[E, A]): Predicate[E, A] =
And(this, that)
def or(that: Predicate[E, A]): Predicate[E, A] =
Or(this, that)
/**
* This part is for Kleislis
* #return
*/
def run(implicit s: Semigroup[E]): A => Either[E, A] =
(a: A) => this(a).toEither
def apply(a: A)(implicit s: Semigroup[E]): Validated[E, A] =
this match {
case Pure(func) =>
func(a)
case And(left, right) => (left(a), right(a)).mapN((_, _) => a)
case Or(left, right) =>
left(a) match {
case Valid(_) => Valid(a)
case Invalid(e1) =>
right(a) match {
case Valid(_) => Invalid(e1)
case Invalid(e2) => Invalid(e1 |+| e2)
}
}
}
}
object Predicate {
final case class And[E, A](left: Predicate[E, A], right: Predicate[E, A]) extends Predicate[E, A]
final case class Or[E, A](left: Predicate[E, A], right: Predicate[E, A]) extends Predicate[E, A]
final case class Pure[E, A](func: A => Validated[E, A]) extends Predicate[E, A]
def apply[E, A](f: A => Validated[E, A]): Predicate[E, A] = Pure(f)
def lift[E, A](err: E, fn: A => Boolean): Predicate[E, A] = Pure(a => if(fn(a)) a.valid else err.invalid)
}
object FinalRecapPredicate {
type Errors = NonEmptyList[String]
def error(s: String): NonEmptyList[String] = NonEmptyList(s, Nil)
type Result[A] = Either[Errors, A]
type Check[A, B] = Kleisli[Result, A, B]
def check[A, B](func: A => Result[B]): Check[A, B] = Kleisli(func)
def checkPred[A](pred: Predicate[Errors, A]): Check[A, A] =
Kleisli[Result, A, A](pred.run)
def longerThan(n: Int): Predicate[Errors, String] =
Predicate.lift(
error(s"Must be longer than $n characters"),
str => str.length > n
)
val alphanumeric: Predicate[Errors, String] =
Predicate.lift(
error(s"Must be all alphanumeric characters"),
str => str.forall(_.isLetterOrDigit)
)
def contains(char: Char): Predicate[Errors, String] =
Predicate.lift(
error(s"Must contain the character $char"),
str => str.contains(char)
)
def containsOnce(char: Char): Predicate[Errors, String] =
Predicate.lift(
error(s"Must contain the character $char only once"),
str => str.count(_ == char) == 1
)
val checkUsername: Check[String, String] = checkPred(longerThan(3) and alphanumeric)
val splitEmail: Check[String, (String, String)] = check(_.split('#') match {
case Array(name, domain) =>
Right((name, domain))
case _ =>
Left(error("Must contain a single # character"))
})
val checkLeft: Check[String, String] = checkPred(longerThan(0))
val checkRight: Check[String, String] = checkPred(longerThan(3) and contains('.'))
val joinEmail: Check[(String, String), String] =
check {
case (l, r) => (checkLeft(l), checkRight(r)).mapN(_ + "#" + _)
}
val checkEmail: Check[String, String] = splitEmail andThen joinEmail
final case class User(username: String, email: String)
def createUser(username: String, email: String): Either[Errors, User] =
(checkUsername.run(username),
checkEmail.run(email)).mapN(User)
def main(args: Array[String]): Unit = {
println(createUser("", "noel#underscore.io#io"))
}
}
It supposes the code should end up with the error message Left(NonEmptyList(Must be longer than 3 characters), Must contain a single # character) But what I actually is Left(NonEmptyList(Must be longer than 3 characters))
Obviously, it does not work as expected. It fails fast instead of accumulating errors... How to fix that plz? (I've spent hours now and can't get a workaround)
This is the "problematic" part:
def createUser(username: String, email: String): Either[Errors, User] =
(checkUsername.run(username),
checkEmail.run(email)).mapN(User)
You are combining a tuple of Results, where
type Result[A] = Either[Errors, A]
This means you are really doing a mapN on a pair of Eithers, an operation provided by the Semigroupal type class. This operation will not accumulate results.
There are several reasons for this, but one that I find particularly important is the preserving of behaviour if we find ourselves using a Semigroupal / Applicative which also happens to be a Monad. Why is that a problem? Because Monads are sequencing operations, making each step depend on the previous one, and having "fail early" semantics. When using some Monad, one might expect those semantics to be preserved when using constructs from the underlying Applicative (every Monad is also an Applicative). In that case, if the implementation of Applicative used "accumulation" semantics instead of "fail early" semantics, we would ruin some important laws like referential transparency.
You can use a parallel version of mapN, called parMapN, whose contract guarantees that the implementation will be evaluating all results in parallel. This means that it definitely cannot be expected to have the "fail early" semantics, and accumulating results is fine in that case.
Note that Validated accumulates results as well, usually in a NonEmptyList or a NonEmptyChain. This is probably why you expected to see your accumulated results; the only problem is, you were not using Validated values in the "problematic" part of your code, but raw Eithers instead.
Here's some simple code that demonstrates the above concepts:
import cats.data._
import cats.implicits._
val l1: Either[String, Int] = Left("foo")
val l2: Either[String, Int] = Left("bar")
(l1, l2).mapN(_ + _)
// Left(foo)
(l1, l2).parMapN(_ + _)
// Left(foobar)
val v1: ValidatedNel[String, Int] = l1.toValidatedNel
val v2: ValidatedNel[String, Int] = l2.toValidatedNel
(v1, v2).mapN(_ + _)
// Invalid(NonEmptyList(foo, bar))
I have 4 methods with one logic and 4 possible type mapping:
def convertStringToString(in: String): String = ???
def convertIntToString(in: Int): String = ???
def convertIntToInt(in: Int): Int = ???
def convertStringToInt(in: String): Int = ???
I want to generalize input and output type and write logic in one methods. Tried to generelize input parameter:
def convertToInt[IN](in: IN): Int = in match {
case x: String if x.forall(_.isDigit) => x.toInt
case y: Int => y
case _ => 0
}
def convertToString[IN](in: IN): String = convertToInt[IN](in).toString
Could you help me to generalize second:
def convertToInt[IN, OUT](in: IN): OUT = ???
If you really wanted to, you could have something typeclass-based:
def convert[I, O](in: I)(implicit c: ConversionRule[I, O]): O = {
if (c.isConvertible(in)) c.convert(in)
else c.zero
}
trait ConversionRule[I, O] {
def isConvertible(in: I): Boolean
def convert(in: I): O
def zero: O // Could possibly derive the zero from, e.g., a cats Monoid instance where such exists
}
The eagle-eyed may notice that the isConvertible/convert methods match the contract of PartialFunction[I, O]'s isDefinedAt/apply, so may as well just use PartialFunction (and rewrite convert with isDefinedAt/apply)
trait ConversionRule[I, O] extends PartialFunction[I, O] {
def zero: O
}
zero can be implemented in terms of PartialFunction.applyOrElse, but for the case where zero is constant (which is the case where referential transparency is preserved), this is much faster.
Smart constructors can be defined:
object ConversionRule {
def apply[I, O](zeroValue: O)(pf: PartialFunction[I, O]): ConversionRule[I, O] =
new ConversionRule[I, O] {
override def apply(i: I): O = pf(i)
override def isDefinedAt(i: I): Boolean = pf.isDefinedAt(i)
val zero: O = zeroValue
}
def totalConversion[I, O](f: I => O): ConversionRule[I, O] =
new ConversionRule[I, O] {
override def apply(i: I) = f(i)
override def isDefinedAt(i: I) = true
override def zero: O = throw new AssertionError("Should not call since conversion is defined")
}
// Might want to put this in a `LowPriorityImplicits` trait which this object extends
implicit def identityConversion[I]: ConversionRule[I, I] =
totalConversion(identity)
}
identityConversion means that a convertIntToInt gets automatically generated.
convertStringToInt can then be defined as
implicit val stringToIntConversion = ConversionRule[String, Int](0) {
case x if x.forAll(_.isDigit) => x.toInt
}
One can define a toString based conversion (basically the non-lawful Show proposed for alleycats):
implicit def genericToString[I]: ConversionRule[I, String] =
ConversionRule.totalConversionRule(_.toString)
And it should then be possible to define a stringViaInt ConversionRule derivation like:
implicit def stringViaInt[I, O](implicit toInt: ConversionRule[I, Int]): ConversionRule[I, String] =
convert(convert(in)(toInt))
The only really useful thing this provides is an opt-in to usage of implicit conversions. Whether that's enough of a gain to justify? shrug
(Disclaimer: only the scala compiler in my head has attempted to compile this)
Edit:
Last revision was deemed unhelpful as it did not contain necessary information that help narrow down my issue. hence the need to also include the AST.
Below is a library in its entirety that allows parsing and writing of play-json's json based on user defined schema; Similar to what Scala's slick offers for database columns to some extent:
import scala.language.higherKinds
import play.api.libs.functional.syntax._
import play.api.libs.json._
import scala.language.{higherKinds, implicitConversions}
type PathNodes = List[PathNode]
sealed trait Field[A] {
def pathNodes: PathNodes
def jsPath: JsPath = JsPath(pathNodes)
def relativePath: JsPath = JsPath(List(pathNodes.last))
def format: Format[A]
def nestedFormatter(path: JsPath): OFormat[A]
def nestedFormat: OFormat[A] = nestedFormatter(relativePath)
}
case class PlainField[A: Format](prefix: PathNodes) extends Field[A] {
override def pathNodes: PathNodes = prefix
def format: Format[A] = implicitly[Format[A]]
override def nestedFormatter(path: JsPath): OFormat[A] = path.format(format)
}
abstract class JsonSchema[T](val _prefix: PathNodes) extends Field[T] with SchemaExtensionMethods {
override def pathNodes: PathNodes = _prefix
def format: OFormat[T]
protected def plain[A: Format](name: String): PlainField[A] = PlainField[A](_prefix :+ KeyPathNode(name))
protected def nested[N](name: String, factory: PathNodes => N): N = factory(_prefix :+ KeyPathNode(name))
protected def nested[B, G <: JsonSchema[B]](name: String)(implicit sm: HasJsonSchema[B, G]): G = sm.apply(_prefix :+ KeyPathNode(name))
override def nestedFormatter(path: JsPath): OFormat[T] = path.format(format)
}
case class Optional[F, A](field: F)(implicit ev: F <:< Field[A]) extends Field[Option[A]] {
override def pathNodes: PathNodes = field.pathNodes
override def format: Format[Option[A]] = {
implicit val writes: Writes[Option[A]] = JsPath.writeNullable(field.format)
implicit val reads: Reads[Option[A]] = JsPath.readNullable(field.format)
implicitly[Format[Option[A]]]
}
def map[G, B](f: F => G)(implicit ev: G <:< Field[B]): Optional[G, B] = new Optional[G, B](f(field))
def flatMap[G <: Field[B], B](f: F => Optional[G, B]): Optional[G, B] = f(field)
override def nestedFormatter(path: JsPath): OFormat[Option[A]] = path.formatNullable(field.format)
}
case class Collection[F, A](field: F)(implicit ev: F <:< Field[A], repath: Repath[F]) extends Field[Seq[A]] {
override def pathNodes: PathNodes = field.pathNodes
override def format: Format[Seq[A]] = {
implicit val writes: Writes[Seq[A]] = Writes.seq(field.format)
implicit val reads: Reads[Seq[A]] = Reads.seq(field.format)
implicitly[Format[Seq[A]]]
}
def apply(idx: Int): F = implicitly[Repath[F]].apply(field, IdxPathNode(idx))
override def nestedFormatter(path: JsPath): OFormat[Seq[A]] = path.format(format)
}
class FormatExtensionMethods[T](val arg: T) {
def <>[A, B, Fun](apply: Fun, unapply: B => Option[A])(implicit jss: JsonShape[A, B, T, Fun]): OFormat[B] = jss.format(arg, apply, unapply andThen (_.get))
}
class FieldExtensionMethods[F](val field: F) {
def optional[A](implicit ev: F <:< Field[A]): Optional[F, A] = new Optional[F, A](field)
def sequence[A](implicit ev: F <:< Field[A], repath: Repath[F]): Collection[F, A] = new Collection[F, A](field)
}
trait SchemaExtensionMethods {
implicit def formatExtensionMethods[M](t: M): FormatExtensionMethods[M] = new FormatExtensionMethods[M](t)
implicit def fieldExtensionMethods[M, A](t: M): FieldExtensionMethods[M] = new FieldExtensionMethods[M](t)
}
trait Repath[F] {
def apply(f: F, node: PathNode): F
}
object Repath {
implicit def plain[T]: Repath[PlainField[T]] = new Repath[PlainField[T]] {
override def apply(t: PlainField[T], node: PathNode): PlainField[T] =
PlainField[T](t.pathNodes :+ node)(t.format)
}
implicit def schema[S <: JsonSchema[_]](implicit sm: HasJsonSchema[_, S]): Repath[S] = new Repath[S] {
override def apply(t: S, node: PathNode): S =
sm.apply(t.pathNodes :+ node)
}
implicit def option[F <: Field[T] : Repath, T]: Repath[Optional[F, T]] = new Repath[Optional[F, T]] {
override def apply(t: Optional[F, T], node: PathNode): Optional[F, T] =
new Optional[F, T](implicitly[Repath[F]].apply(t.field, node))
}
implicit def sequence[F <: Field[T] : Repath, T]: Repath[Collection[F, T]] = new Repath[Collection[F, T]] {
override def apply(t: Collection[F, T], node: PathNode): Collection[F, T] =
new Collection[F, T](implicitly[Repath[F]].apply(t.field, node))
}
}
trait JsonShape[A, B, -T, Func] {
def format(t: T, apply: Func, unapply: B => A): OFormat[B]
}
object JsonShape {
type F[T] = Field[T]
implicit def cc1[A, B]: JsonShape[A, B, F[A], (A) => B] = (t: F[A], apply: (A) => B, unapply: B => A) => {
val name = t.pathNodes.last.asInstanceOf[KeyPathNode].key
OFormat[B](
Reads[B](jsv => (jsv \ name).validate[A](t.format).map(apply)),
OWrites[B](b => JsObject(Map(name -> Json.toJson(unapply(b))(t.format))))
)
}
implicit def cc2[T1, T2, B]: JsonShape[(T1, T2), B, (F[T1], F[T2]), (T1, T2) => B] = (t: (F[T1], F[T2]), apply: (T1, T2) => B, unapply: B => (T1, T2)) => {
(
t._1.nestedFormat and
t._2.nestedFormat
) (apply, unapply)
}
implicit def cc3[T1, T2, T3, B]: JsonShape[(T1, T2, T3), B, (F[T1], F[T2], F[T3]), (T1, T2, T3) => B] = (t: (F[T1], F[T2], F[T3]), apply: (T1, T2, T3) => B, unapply: B => (T1, T2, T3)) => {
(
t._1.nestedFormat and
t._2.nestedFormat and
t._3.nestedFormat
) (apply, unapply)
}
//this goes up to 22
}
abstract class HasJsonSchema[T, +S <: JsonSchema[T]](val apply: PathNodes => S) extends OFormat[T] {
val root: S = apply(Nil)
def format: OFormat[T] = root.format
def writes(o: T): JsObject = root.format.writes(o)
def reads(json: JsValue): JsResult[T] = root.format.reads(json)
}
Now let's write a small piece of client code that reproduce the issue:
case class MessageSchema(prefix: PathNodes) extends JsonSchema[Message](prefix) {
def underlying = plain[String]("underlying")
//def underlying = plain[String]("underlying").optional if I wanted the field to be Option[String]
//def underlying = plain[String]("underlying").sequence if I wanted the field to be Seq[String]
override def format = underlying <> (Message.apply _, Message.unapply)
}
case class Message(underlying: String)
object Message {
implicit object sm extends HasJsonSchema[Message, MessageSchema](MessageSchema.apply)
}
case class LanguageTaggedSchema[T, S <: JsonSchema[T]](prefix: PathNodes)(implicit evT: HasJsonSchema[T, S]) extends JsonSchema[LanguageTagged[T]](prefix) {
def lang = plain[String]("lang")
def data: S = nested("data")(evT)
def format = (lang, data) <> (LanguageTagged.apply[T] _, LanguageTagged.unapply[T])
}
case class LanguageTagged[T](lang: String, data: T)
object LanguageTagged {
implicit def schemaMapper[T, S <: JsonSchema[T]](implicit ev: HasJsonSchema[T, S]): HasJsonSchema[LanguageTagged[T], LanguageTaggedSchema[T, S]] =
new HasJsonSchema[LanguageTagged[T], LanguageTaggedSchema[T, S]](LanguageTaggedSchema.apply[T, S]) {}
}
def toJson[T, S <: JsonSchema[T]](a: T)(implicit ev: HasJsonSchema[T, S]): JsValue = Json.toJson(a)(ev.format)
toJson(Message("hi")) //Ok!
toJson(LanguageTagged("en", Message("hi"))) //Ok!
//or simply write
Json.toJson(LanguageTagged("en", Message("hi")))
//and if i wanted to traverse a json path i would do:
val schema = implicitly[HasJsonSchema[LanguageTagged[Message],LanguageTaggedSchema[Message,MessageSchema]]].root
schema.data.underlying.jsPath
//prints: res2: play.api.libs.json.JsPath = /data/underlying
//Now to where the problem starts:
def getSchema[T, S <: JsonSchema[T]](a: T)(implicit ev: HasJsonSchema[T, S]): S = ev.root
getSchema(Message("hi")) //Ok!
getSchema(LanguageTagged("en", Message("hi"))) //Not Ok but why?
//Error:(211, 11) could not find implicit value for
//parameter ev: A$A6.this.HasJsonSchema[A$A6.this.LanguageTagged[A$A6.this.Message],S]
//getSchema(LanguageTagged("en", Message("hi")));//
//^
I have a huge suspicion that the compiler runs into issues because of the bounded type of S inHasJsonSchema[T, S <: JsonSchema[T]] when infering the implicit type S. and so far only in that specific situation as shown on the last line of all the code. as a dubugging attempt I created a similar situation and realized that if the type S was not bounded I wouldn't have this issue. Any sort of solution that refactors the code such that it doesn't depend on bounded types or one that simply solves the implicit resolution is appreciated
What You're trying to achieve cannot be done with subtyping. You should use type-classes instead, a more in-depth explanation:
http://danielwestheide.com/blog/2013/02/06/the-neophytes-guide-to-scala-part-12-type-classes.html
How can I apply implement the following function?
def wrapIntoOption(state: State[S, A]): State[Option[S], Option[A]]
The bigger picture is this:
case class Engine(cylinders: Int)
case class Car(engineOpt: Option[Engine])
val engineOptLens = Lens.lensu((c, e) => c.copy(engineOpt = e), _.engineOpt)
def setEngineCylinders(count: Int): State[Engine, Int] = State { engine =>
(engine.copy(cylinders = count), engine.cylinders)
}
def setEngineOptCylinders(count: Int): State[Option[Engine], Option[Int]] = {
??? // how to reuse setEngineCylinders?
}
def setCarCylinders(count: Int): State[Car, Option[Int]] = {
engineOptLens.lifts(setEngineOptCylinders)
}
Is there nicer way to deal with Option properties?
One way to create the wrapIntoOption function would be to call run on the passed State[S, A] and convert the resulting (S, A) tuple into (Option[S], Option[A]).
import scalaz.State
import scalaz.std.option._
import scalaz.syntax.std.option._
def wrapIntoOption[S, A](state: State[S, A]): State[Option[S], Option[A]] =
State(_.fold((none[S], none[A])){ oldState =>
val (newState, result) = state.run(oldState)
(newState.some, result.some)
})
You could then define setEngineOptCylinders as :
def setEngineOptCylinders(count: Int): State[Option[Engine], Option[Int]] =
wrapIntoOption(setEngineCylinders(count))
Which you can use as :
scala> val (newEngine, oldCylinders) = setEngineOptCylinders(8).run(Engine(6).some)
newEngine: Option[Engine] = Some(Engine(8))
oldCylinders: Option[Int] = Some(6)
I was wondering if anyone could provide some insight on a problem I'm having. I've made a gist with some code and explanation of my problem: https://gist.github.com/tbrown1979/9993f07c8f4fa2786c83
Basically I'm trying to make something that will allow me to convert List[String] to a case class. I've made a Reader that will allow me to do so, but I've run into the issue where a Reader defined for a case class can't contain a reader for a separate case class.
Looking at the 'non-working example' below - I encounter an issue where, when reading, I don't know how many items to pull out of the list. With Bar, which holds a Test, I would need to pull 2 elements out (because Test has two parameters). Is there a way for me to know the amount of fields a case class has just from its type? Is there a better way to do this?
Here is an example of how to use the Reader. I've included a non-working example as well.
////Working Example////
case class Foo(a: Int, s: String)
object Foo {
implicit val FooReader : Reader[Foo] =
Reader[Int :: String :: HNil].map(Generic[Foo].from _)
}
val read: ValidationNel[String, Foo] = Reader.read[Foo](List("12","text"))
println(read)//Success(Foo(12, "text"))
///////////////////////////
////Non-working Example////
case class Test(a: Int, b: String)
object Test {
implicit val TestReader: Reader[Test] =
Reader[Int :: String :: HNil].map(Generic[Test].from _)
}
case class Bar(c: Test)
object Bar {
implicit val BarReader: Reader[Bar] =
Reader[Test :: HNil].map(Generic[Bar].from _)
}
val barRead = Reader.read[Bar](List("21", "someString"))
println(barRead) //Failure(NonEmptyList("Invalid String: List()", "Exepected empty, but contained value"))
//////////////////////////
Something like this works for me (modification of this)
object ShapelessStringToTypeConverters {
import cats._, implicits._, data.ValidatedNel
import mouse._, string._, option._
import shapeless._, labelled._
private type Result[A] = ValidatedNel[ParseFailure, A]
case class ParseFailure(error: String)
trait Convert[V] {
def parse(input: String): Result[V]
}
object Convert {
def to[V](input: String)(implicit C: Convert[V]): Result[V] =
C.parse(input)
def instance[V](body: String => Result[V]): Convert[V] = new Convert[V] {
def parse(input: String): Result[V] = body(input)
}
implicit def booleans: Convert[Boolean] =
Convert.instance(
s =>
s.parseBooleanValidated
.leftMap(e => ParseFailure(s"Not a Boolean ${e.getMessage}"))
.toValidatedNel)
implicit def ints: Convert[Int] =
Convert.instance(
s =>
s.parseIntValidated
.leftMap(e => ParseFailure(s"Not an Int ${e.getMessage}"))
.toValidatedNel)
implicit def longs: Convert[Long] =
Convert.instance(
s =>
s.parseLongValidated
.leftMap(e => ParseFailure(s"Not an Long ${e.getMessage}"))
.toValidatedNel)
implicit def doubles: Convert[Double] =
Convert.instance(
s =>
s.parseDoubleValidated
.leftMap(e => ParseFailure(s"Not an Double ${e.getMessage}"))
.toValidatedNel)
implicit def strings: Convert[String] = Convert.instance(s => s.validNel)
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
sealed trait SchemaMap[A] {
def readFrom(input: Map[String, String]): ValidatedNel[ParseFailure, A]
}
object SchemaMap {
def of[A](implicit s: SchemaMap[A]): SchemaMap[A] = s
private def instance[A](body: Map[String, String] => Result[A]): SchemaMap[A] = new SchemaMap[A] {
def readFrom(input: Map[String, String]): Result[A] =
body(input)
}
implicit val noOp: SchemaMap[HNil] =
SchemaMap.instance(_ => HNil.validNel)
implicit def parsing[K <: Symbol, V: Convert, T <: HList](implicit key: Witness.Aux[K], next: SchemaMap[T]): SchemaMap[FieldType[K, V] :: T] =
SchemaMap.instance { input =>
val fieldName = key.value.name
val parsedField = input
.get(fieldName)
.cata(entry => Convert.to[V](entry), ParseFailure(s"$fieldName is missing").invalidNel)
.map(f => field[K](f))
(parsedField, next.readFrom(input)).mapN(_ :: _)
}
implicit def classes[A, R <: HList](implicit repr: LabelledGeneric.Aux[A, R], schema: SchemaMap[R]): SchemaMap[A] =
SchemaMap.instance { input =>
schema.readFrom(input).map(x => repr.from(x))
}
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
sealed trait SchemaList[A] {
def readFrom(input: List[String]): ValidatedNel[ParseFailure, A]
}
object SchemaList {
def of[A](implicit s: SchemaList[A]): SchemaList[A] = s
private def instance[A](body: List[String] => Result[A]): SchemaList[A] = new SchemaList[A] {
def readFrom(input: List[String]): Result[A] = body(input)
}
implicit val noOp: SchemaList[HNil] =
SchemaList.instance(_ => HNil.validNel)
implicit def parsing[K <: Symbol, V: Convert, T <: HList](implicit key: Witness.Aux[K], next: SchemaList[T]): SchemaList[FieldType[K, V] :: T] =
SchemaList.instance { input =>
val fieldName = key.value.name
val parsedField = input
.headOption
.cata(entry => Convert.to[V](entry), ParseFailure(s"$fieldName is missing").invalidNel)
.map(f => field[K](f))
(parsedField, next.readFrom(input.tail)).mapN(_ :: _)
}
implicit def classes[A, R <: HList](implicit repr: LabelledGeneric.Aux[A, R], schema: SchemaList[R]): SchemaList[A] =
SchemaList.instance { input =>
schema.readFrom(input).map(x => repr.from(x))
}
}
}
/*
case class Foo(a: String, b: Int, c: Boolean)
def m: Map[String, String] = Map("a" -> "hello", "c" -> "true", "b" -> "100")
def e: Map[String, String] = Map("c" -> "true", "b" -> "a100")
val result = SchemaMap.of[Foo].readFrom(m)
val lst = List("145164983", "0.01862523", "16.11681596", "21:38:57", "bid")
case class Trade0(tid: Long, price: Double, amount: Double, time: String, tpe: String)
val result2 = SchemaList.of[Trade0].readFrom(lst)
*/